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1
Machine Learning Methods for Data-Driven Decision Making: Contextual Optimization, Causal Inference, and Algorithmic Fairness
Machine Learning Methods for Data-Driven Decision Making: Contextual Optimization, Causal Inference, and Algorithmic Fairness
Mao, Xiaojie. | Cornell University | 2021
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2
Hormonal Regulation of Iron Partitioning during Pregnancy
Hormonal Regulation of Iron Partitioning during Pregnancy
Delaney, Katherine Marie. | Cornell University | 2021
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3
Primitives for Match-Action in Theory and Practice
Primitives for Match-Action in Theory and Practice
Long, Xiang. | Cornell University | 2021
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4
Knowledge of Counterfactuals
Knowledge of Counterfactuals
Sandu, Ana Smaranda. | Cornell University | 2021
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5
Neutron Star Oscillations, Magnetic Fields, and Superfluidity
Neutron Star Oscillations, Magnetic Fields, and Superfluidity
Rau, Peter Brian. | Cornell University | 2021
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6
Complexity, Conditioning, and Saddle Avoidance in Nonsmooth Optimization
Complexity, Conditioning, and Saddle Avoidance in Nonsmooth Optimization
Diaz Diaz, Mateo. | Cornell University | 2021
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7
Definiteness and Quantification: Evidence from Shan
Definiteness and Quantification: Evidence from Shan
Moroney, Mary Rose. | Cornell University | 2021
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8
Evolution of Repetitive DNA: Methodologies, Expansions, and Constraints
Evolution of Repetitive DNA: Methodologies, Expansions, and Constraints
Flynn, Jullien Michelle. | Cornell University | 2021
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9
Are You Ready for It? Examining Security in Contemporary Disaster Preparedness, from Normal to Noachian
Are You Ready for It? Examining Security in Contemporary Disaster Preparedness, from Normal to Noachian
Ellis, Christopher Michael. | Cornell University | 2021
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10
Model Combinations and the Infinitesimal Jackknife: How to Refine Models with Boosting and Quantify Uncertainty
Model Combinations and the Infinitesimal Jackknife: How to Refine Models with Boosting and Quantify Uncertainty
Ghosal, Indrayudh. | Cornell University | 2021
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